{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_ner_CONLL_2003_5class_example.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/tutorial_docs/examples/colab/component_examples/named_entity_recognition_NER/NLU_ner_CONLL_2003_5class_example.ipynb)\n","\n","\n","Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:\n","<br>\n","<br>\n","[ORG **U.N.** ] official [PER **Ekeus** ] heads for [LOC **Baghdad** ] .       \n","<br>\n","\n","https://www.aclweb.org/anthology/W03-0419.pdf    \n","CoNLL-2003 is a NER dataset that available in English and German. NLU provides pretrained languages for both of these languages.\n","\n","It features **5 classes** of tags, **LOC (location)** , **ORG(Organisation)**, **PER(Persons)** and the forth which describes all the named entities which do not belong to any of the thre previously mentioned tags **(MISC)**.        \n","The fifth class **(O)** is used for tokens which belong to no named entity.\n","\n","\n","\n","\n","\n","|Tag | \tDescription |\n","|------|--------------|\n","|PER | A person like **Jim** or **Joe** |\n","|ORG | An organisation like **Microsoft** or **PETA**|\n","|LOC | A location like **Germany**|\n","|MISC | Anything else like **Playstation** |\n","|O| Everything that is not an entity. | \n","\n","\n","The shared task of [CoNLL-2003 concerns](https://www.clips.uantwerpen.be/conll2003/) language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. For each language, additional information (lists of names and non-annotated data) will be supplied as well. The challenge for the participants is to find ways of incorporating this information in their system.\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1649992899704,"user_tz":-300,"elapsed":94666,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"7f1155bd-1892-494d-ce99-e4aedffabac0"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","  \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-04-15 03:20:03--  https://setup.johnsnowlabs.com/nlu/colab.sh\n","Resolving setup.johnsnowlabs.com (setup.johnsnowlabs.com)... 51.158.130.125\n","Connecting to setup.johnsnowlabs.com (setup.johnsnowlabs.com)|51.158.130.125|:443... connected.\n","HTTP request sent, awaiting response... 302 Moved Temporarily\n","Location: https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh [following]\n","--2022-04-15 03:20:04--  https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1665 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","-                   100%[===================>]   1.63K  --.-KB/s    in 0s      \n","\n","2022-04-15 03:20:04 (37.1 MB/s) - written to stdout [1665/1665]\n","\n","Installing  NLU 3.4.3rc2 with  PySpark 3.0.3 and Spark NLP 3.4.2 for Google Colab ...\n","Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Get:3 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Get:4 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Ign:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release [696 B]\n","Hit:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n","Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Hit:11 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:13 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n","Get:14 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Get:15 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [953 kB]\n","Hit:16 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Get:17 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,947 kB]\n","Get:18 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,490 kB]\n","Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,134 kB]\n","Get:20 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,695 kB]\n","Get:21 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [996 kB]\n","Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,268 kB]\n","Get:23 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [45.3 kB]\n","Fetched 13.8 MB in 3s (4,341 kB/s)\n","Reading package lists... Done\n","tar: spark-3.0.2-bin-hadoop2.7.tgz: Cannot open: No such file or directory\n","tar: Error is not recoverable: exiting now\n","\u001b[K     |████████████████████████████████| 209.1 MB 52 kB/s \n","\u001b[K     |████████████████████████████████| 142 kB 73.8 MB/s \n","\u001b[K     |████████████████████████████████| 505 kB 58.6 MB/s \n","\u001b[K     |████████████████████████████████| 198 kB 49.3 MB/s \n","\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting nlu_tmp==3.4.3rc10\n","  Downloading nlu_tmp-3.4.3rc10-py3-none-any.whl (510 kB)\n","\u001b[K     |████████████████████████████████| 510 kB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: pyarrow>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (6.0.1)\n","Requirement already satisfied: dataclasses in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (0.6)\n","Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.3.5)\n","Requirement already satisfied: spark-nlp<3.5.0,>=3.4.2 in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (3.4.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from nlu_tmp==3.4.3rc10) (1.21.5)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.5->nlu_tmp==3.4.3rc10) (2018.9)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.3.5->nlu_tmp==3.4.3rc10) (1.15.0)\n","Installing collected packages: nlu-tmp\n","Successfully installed nlu-tmp-3.4.3rc10\n"]}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes NER easy. \n","\n","You just need to load the NER model via ner.load() and predict on some dataset.    \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":681},"id":"pmpZSNvGlyZQ","executionInfo":{"status":"ok","timestamp":1649992963376,"user_tz":-300,"elapsed":63692,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"022b928d-605c-4595-b86c-5c7ea593f12c"},"source":["import nlu \n","\n","example_text =  [\"A person like Jim or Joe\", \n"," \"An organisation like Microsoft or PETA\",\n"," \"A location like Germany\",\n"," \"Anything else like Playstation\", \n"," \"Person consisting of multiple tokens like Angela Merkel or Donald Trump\",\n"," \"Organisations consisting of multiple tokens like JP Morgan\",\n"," \"Locations consiting of multiple tokens like Los Angeles\", \n"," \"Anything else made up of multiple tokens like Super Nintendo\",]\n","\n","nlu.load('ner').predict(example_text)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"]},{"output_type":"execute_result","data":{"text/plain":["                                            document    entities_ner  \\\n","0                           A person like Jim or Joe             Jim   \n","0                           A person like Jim or Joe             Joe   \n","1             An organisation like Microsoft or PETA       Microsoft   \n","1             An organisation like Microsoft or PETA            PETA   \n","2                            A location like Germany         Germany   \n","3                     Anything else like Playstation     Playstation   \n","4  Person consisting of multiple tokens like Ange...   Angela Merkel   \n","4  Person consisting of multiple tokens like Ange...    Donald Trump   \n","5  Organisations consisting of multiple tokens li...       JP Morgan   \n","6  Locations consiting of multiple tokens like Lo...     Los Angeles   \n","7  Anything else made up of multiple tokens like ...  Super Nintendo   \n","\n","  entities_ner_class entities_ner_confidence  \\\n","0             PERSON                  0.9966   \n","0             PERSON                  0.6787   \n","1                ORG                  0.9981   \n","1                ORG                  0.9813   \n","2                GPE                  0.9595   \n","3            PRODUCT                  0.6818   \n","4             PERSON                 0.97305   \n","4             PERSON                   0.903   \n","5                ORG                 0.60135   \n","6                GPE              0.75864995   \n","7            PRODUCT                 0.38125   \n","\n","                                  word_embedding_ner  \n","0  [[-0.2708599865436554, 0.04400600120425224, -0...  \n","0  [[-0.2708599865436554, 0.04400600120425224, -0...  \n","1  [[-0.4214000105857849, -0.18796999752521515, 0...  \n","1  [[-0.4214000105857849, -0.18796999752521515, 0...  \n","2  [[-0.2708599865436554, 0.04400600120425224, -0...  \n","3  [[-0.029784999787807465, 0.08645900338888168, ...  \n","4  [[0.3870899975299835, 0.3262900114059448, 0.64...  \n","4  [[0.3870899975299835, 0.3262900114059448, 0.64...  \n","5  [[-0.19327999651432037, 0.6523399949073792, -1...  \n","6  [[0.06345599889755249, -0.042142000049352646, ...  \n","7  [[-0.029784999787807465, 0.08645900338888168, ...  "],"text/html":["\n","  <div id=\"df-562f403f-a56b-4ab1-8b15-b9809406f750\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_ner</th>\n","      <th>entities_ner_class</th>\n","      <th>entities_ner_confidence</th>\n","      <th>word_embedding_ner</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>A person like Jim or Joe</td>\n","      <td>Jim</td>\n","      <td>PERSON</td>\n","      <td>0.9966</td>\n","      <td>[[-0.2708599865436554, 0.04400600120425224, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>A person like Jim or Joe</td>\n","      <td>Joe</td>\n","      <td>PERSON</td>\n","      <td>0.6787</td>\n","      <td>[[-0.2708599865436554, 0.04400600120425224, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>An organisation like Microsoft or PETA</td>\n","      <td>Microsoft</td>\n","      <td>ORG</td>\n","      <td>0.9981</td>\n","      <td>[[-0.4214000105857849, -0.18796999752521515, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>An organisation like Microsoft or PETA</td>\n","      <td>PETA</td>\n","      <td>ORG</td>\n","      <td>0.9813</td>\n","      <td>[[-0.4214000105857849, -0.18796999752521515, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>A location like Germany</td>\n","      <td>Germany</td>\n","      <td>GPE</td>\n","      <td>0.9595</td>\n","      <td>[[-0.2708599865436554, 0.04400600120425224, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Anything else like Playstation</td>\n","      <td>Playstation</td>\n","      <td>PRODUCT</td>\n","      <td>0.6818</td>\n","      <td>[[-0.029784999787807465, 0.08645900338888168, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Person consisting of multiple tokens like Ange...</td>\n","      <td>Angela Merkel</td>\n","      <td>PERSON</td>\n","      <td>0.97305</td>\n","      <td>[[0.3870899975299835, 0.3262900114059448, 0.64...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Person consisting of multiple tokens like Ange...</td>\n","      <td>Donald Trump</td>\n","      <td>PERSON</td>\n","      <td>0.903</td>\n","      <td>[[0.3870899975299835, 0.3262900114059448, 0.64...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Organisations consisting of multiple tokens li...</td>\n","      <td>JP Morgan</td>\n","      <td>ORG</td>\n","      <td>0.60135</td>\n","      <td>[[-0.19327999651432037, 0.6523399949073792, -1...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Locations consiting of multiple tokens like Lo...</td>\n","      <td>Los Angeles</td>\n","      <td>GPE</td>\n","      <td>0.75864995</td>\n","      <td>[[0.06345599889755249, -0.042142000049352646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Anything else made up of multiple tokens like ...</td>\n","      <td>Super Nintendo</td>\n","      <td>PRODUCT</td>\n","      <td>0.38125</td>\n","      <td>[[-0.029784999787807465, 0.08645900338888168, ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-562f403f-a56b-4ab1-8b15-b9809406f750')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    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Barclays   \n","0   Barclays misled shareholders and the public ab...             about one   \n","0   Barclays misled shareholders and the public ab...          BBC Panorama   \n","1   The bank announced in 2008 that Manchester Cit...                  2008   \n","1   The bank announced in 2008 that Manchester Cit...       Manchester City   \n","1   The bank announced in 2008 that Manchester Cit...        Sheikh Mansour   \n","2   But the BBC found that the money, which helped...                   BBC   \n","2   But the BBC found that the money, which helped...              Barclays   \n","2   But the BBC found that the money, which helped...               British   \n","2   But the BBC found that the money, which helped...             Abu Dhabi   \n","3   Barclays said the mistake in its accounts was ...              Barclays   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                   RBS   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...  Lloyds TSB, Barclays   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                  2008   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                  Gulf   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...                 Qatar   \n","4   Unlike RBS and Lloyds TSB, Barclays narrowly a...            Abu Dhabi.   \n","5   The S&P 500's price to earnings multiple is 71...                   S&P   \n","5   The S&P 500's price to earnings multiple is 71...                 500's   \n","5   The S&P 500's price to earnings multiple is 71...                   71%   \n","5   The S&P 500's price to earnings multiple is 71...              Apple's,   \n","5   The S&P 500's price to earnings multiple is 71...                 Apple   \n","5   The S&P 500's price to earnings multiple is 71...                  $840   \n","5   The S&P 500's price to earnings multiple is 71...                   52%   \n","6   Alice has a cat named Alice and also a dog nam...                 Alice   \n","6   Alice has a cat named Alice and also a dog nam...                 Alice   \n","6   Alice has a cat named Alice and also a dog nam...                 Alice   \n","6   Alice has a cat named Alice and also a dog nam...                Alice,   \n","7                            A person like Jim or Joe                   Jim   \n","7                            A person like Jim or Joe                   Joe   \n","8              An organisation like Microsoft or PETA             Microsoft   \n","8              An organisation like Microsoft or PETA                  PETA   \n","9                             A location like Germany               Germany   \n","10                     Anything else like Playstation           Playstation   \n","11  Person consisting of multiple tokens like Ange...         Angela Merkel   \n","11  Person consisting of multiple tokens like Ange...          Donald Trump   \n","12  Organisations consisting of multiple tokens li...             JP Morgan   \n","13  Locations consiting of multiple tokens like Lo...           Los Angeles   \n","14  Anything else made up of multiple tokens like ...        Super Nintendo   \n","\n","   entities_ner_class entities_ner_confidence  \\\n","0                 ORG                  0.9978   \n","0            CARDINAL                  0.7062   \n","0                 ORG                  0.7376   \n","1                DATE                  0.7053   \n","1                 GPE                 0.94465   \n","1              PERSON                 0.85805   \n","2                 ORG                     1.0   \n","2                 ORG                  0.9982   \n","2                NORP                  0.9884   \n","2                 GPE                 0.59695   \n","3                 ORG                  0.9987   \n","4                 ORG                  0.9999   \n","4                 ORG               0.7895333   \n","4                DATE                  0.8079   \n","4                 LOC                  0.9149   \n","4                 GPE                  0.9996   \n","4                 GPE                  0.5278   \n","5                 ORG                  0.9846   \n","5                DATE                  0.8317   \n","5             PERCENT                  0.9293   \n","5            CARDINAL                  0.2754   \n","5                 ORG                  0.9992   \n","5            CARDINAL                  0.6073   \n","5             PERCENT              0.96535003   \n","6              PERSON                  0.9969   \n","6              PERSON                  0.9601   \n","6              PERSON                  0.9546   \n","6              PERSON                    0.68   \n","7              PERSON                  0.9966   \n","7              PERSON                  0.6787   \n","8                 ORG                  0.9981   \n","8                 ORG                  0.9813   \n","9                 GPE                  0.9595   \n","10            PRODUCT                  0.6818   \n","11             PERSON                 0.97305   \n","11             PERSON                   0.903   \n","12                ORG                 0.60135   \n","13                GPE              0.75864995   \n","14            PRODUCT                 0.38125   \n","\n","                                   word_embedding_ner  \n","0   [[0.044123999774456024, -0.47940999269485474, ...  \n","0   [[0.044123999774456024, -0.47940999269485474, ...  \n","0   [[0.044123999774456024, -0.47940999269485474, ...  \n","1   [[-0.03819400072097778, -0.24487000703811646, ...  \n","1   [[-0.03819400072097778, -0.24487000703811646, ...  \n","1   [[-0.03819400072097778, -0.24487000703811646, ...  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","2   [[-0.05707800015807152, 0.3987399935722351, 0....  \n","3   [[0.044123999774456024, -0.47940999269485474, ...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","4   [[-0.32710000872612, 0.4879100024700165, 0.416...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","5   [[-0.03819400072097778, -0.24487000703811646, ...  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","6   [[0.28501999378204346, -0.4355500042438507, 0....  \n","7   [[-0.2708599865436554, 0.04400600120425224, -0...  \n","7   [[-0.2708599865436554, 0.04400600120425224, -0...  \n","8   [[-0.4214000105857849, -0.18796999752521515, 0...  \n","8   [[-0.4214000105857849, -0.18796999752521515, 0...  \n","9   [[-0.2708599865436554, 0.04400600120425224, -0...  \n","10  [[-0.029784999787807465, 0.08645900338888168, ...  \n","11  [[0.3870899975299835, 0.3262900114059448, 0.64...  \n","11  [[0.3870899975299835, 0.3262900114059448, 0.64...  \n","12  [[-0.19327999651432037, 0.6523399949073792, -1...  \n","13  [[0.06345599889755249, -0.042142000049352646, ...  \n","14  [[-0.029784999787807465, 0.08645900338888168, ...  "],"text/html":["\n","  <div id=\"df-a3326312-8c27-4c37-bf0f-c8f05a250b7c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>document</th>\n","      <th>entities_ner</th>\n","      <th>entities_ner_class</th>\n","      <th>entities_ner_confidence</th>\n","      <th>word_embedding_ner</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Barclays misled shareholders and the public ab...</td>\n","      <td>Barclays</td>\n","      <td>ORG</td>\n","      <td>0.9978</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Barclays misled shareholders and the public ab...</td>\n","      <td>about one</td>\n","      <td>CARDINAL</td>\n","      <td>0.7062</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Barclays misled shareholders and the public ab...</td>\n","      <td>BBC Panorama</td>\n","      <td>ORG</td>\n","      <td>0.7376</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The bank announced in 2008 that Manchester Cit...</td>\n","      <td>2008</td>\n","      <td>DATE</td>\n","      <td>0.7053</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The bank announced in 2008 that Manchester Cit...</td>\n","      <td>Manchester City</td>\n","      <td>GPE</td>\n","      <td>0.94465</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The bank announced in 2008 that Manchester Cit...</td>\n","      <td>Sheikh Mansour</td>\n","      <td>PERSON</td>\n","      <td>0.85805</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>BBC</td>\n","      <td>ORG</td>\n","      <td>1.0</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>Barclays</td>\n","      <td>ORG</td>\n","      <td>0.9982</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>British</td>\n","      <td>NORP</td>\n","      <td>0.9884</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>But the BBC found that the money, which helped...</td>\n","      <td>Abu Dhabi</td>\n","      <td>GPE</td>\n","      <td>0.59695</td>\n","      <td>[[-0.05707800015807152, 0.3987399935722351, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Barclays said the mistake in its accounts was ...</td>\n","      <td>Barclays</td>\n","      <td>ORG</td>\n","      <td>0.9987</td>\n","      <td>[[0.044123999774456024, -0.47940999269485474, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>RBS</td>\n","      <td>ORG</td>\n","      <td>0.9999</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Lloyds TSB, Barclays</td>\n","      <td>ORG</td>\n","      <td>0.7895333</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>2008</td>\n","      <td>DATE</td>\n","      <td>0.8079</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Gulf</td>\n","      <td>LOC</td>\n","      <td>0.9149</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Qatar</td>\n","      <td>GPE</td>\n","      <td>0.9996</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Unlike RBS and Lloyds TSB, Barclays narrowly a...</td>\n","      <td>Abu Dhabi.</td>\n","      <td>GPE</td>\n","      <td>0.5278</td>\n","      <td>[[-0.32710000872612, 0.4879100024700165, 0.416...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>S&amp;P</td>\n","      <td>ORG</td>\n","      <td>0.9846</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>500's</td>\n","      <td>DATE</td>\n","      <td>0.8317</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>71%</td>\n","      <td>PERCENT</td>\n","      <td>0.9293</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>Apple's,</td>\n","      <td>CARDINAL</td>\n","      <td>0.2754</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>Apple</td>\n","      <td>ORG</td>\n","      <td>0.9992</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>$840</td>\n","      <td>CARDINAL</td>\n","      <td>0.6073</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>The S&amp;P 500's price to earnings multiple is 71...</td>\n","      <td>52%</td>\n","      <td>PERCENT</td>\n","      <td>0.96535003</td>\n","      <td>[[-0.03819400072097778, -0.24487000703811646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice</td>\n","      <td>PERSON</td>\n","      <td>0.9969</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice</td>\n","      <td>PERSON</td>\n","      <td>0.9601</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice</td>\n","      <td>PERSON</td>\n","      <td>0.9546</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Alice has a cat named Alice and also a dog nam...</td>\n","      <td>Alice,</td>\n","      <td>PERSON</td>\n","      <td>0.68</td>\n","      <td>[[0.28501999378204346, -0.4355500042438507, 0....</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>A person like Jim or Joe</td>\n","      <td>Jim</td>\n","      <td>PERSON</td>\n","      <td>0.9966</td>\n","      <td>[[-0.2708599865436554, 0.04400600120425224, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>A person like Jim or Joe</td>\n","      <td>Joe</td>\n","      <td>PERSON</td>\n","      <td>0.6787</td>\n","      <td>[[-0.2708599865436554, 0.04400600120425224, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>An organisation like Microsoft or PETA</td>\n","      <td>Microsoft</td>\n","      <td>ORG</td>\n","      <td>0.9981</td>\n","      <td>[[-0.4214000105857849, -0.18796999752521515, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>An organisation like Microsoft or PETA</td>\n","      <td>PETA</td>\n","      <td>ORG</td>\n","      <td>0.9813</td>\n","      <td>[[-0.4214000105857849, -0.18796999752521515, 0...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>A location like Germany</td>\n","      <td>Germany</td>\n","      <td>GPE</td>\n","      <td>0.9595</td>\n","      <td>[[-0.2708599865436554, 0.04400600120425224, -0...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Anything else like Playstation</td>\n","      <td>Playstation</td>\n","      <td>PRODUCT</td>\n","      <td>0.6818</td>\n","      <td>[[-0.029784999787807465, 0.08645900338888168, ...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Person consisting of multiple tokens like Ange...</td>\n","      <td>Angela Merkel</td>\n","      <td>PERSON</td>\n","      <td>0.97305</td>\n","      <td>[[0.3870899975299835, 0.3262900114059448, 0.64...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Person consisting of multiple tokens like Ange...</td>\n","      <td>Donald Trump</td>\n","      <td>PERSON</td>\n","      <td>0.903</td>\n","      <td>[[0.3870899975299835, 0.3262900114059448, 0.64...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Organisations consisting of multiple tokens li...</td>\n","      <td>JP Morgan</td>\n","      <td>ORG</td>\n","      <td>0.60135</td>\n","      <td>[[-0.19327999651432037, 0.6523399949073792, -1...</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Locations consiting of multiple tokens like Lo...</td>\n","      <td>Los Angeles</td>\n","      <td>GPE</td>\n","      <td>0.75864995</td>\n","      <td>[[0.06345599889755249, -0.042142000049352646, ...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Anything else made up of multiple tokens like ...</td>\n","      <td>Super Nintendo</td>\n","      <td>PRODUCT</td>\n","      <td>0.38125</td>\n","      <td>[[-0.029784999787807465, 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chunk"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"UDSAYjadlfdK","executionInfo":{"status":"ok","timestamp":1649993118345,"user_tz":-300,"elapsed":1795,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"19debafb-c2ae-4277-96ce-ef8774186cbe"},"source":["ner_df['entities_ner'].value_counts()[1:].plot.bar(title='Occurence of Named Entity tokens in dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4f0cae2f50>"]},"metadata":{},"execution_count":7},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":310},"id":"rlcEvP9tOSiy","executionInfo":{"status":"ok","timestamp":1649993158366,"user_tz":-300,"elapsed":14,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"6c6e2d84-4db5-4219-be6d-ff6ab6e0d959"},"source":["ner_type_to_viz = 'LOC'\n","ner_df[ner_df.entities_ner_class == ner_type_to_viz]['entities_ner'].value_counts().plot.bar(title='Most often occuring LOC labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4f0c3fed10>"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":391},"executionInfo":{"status":"ok","timestamp":1649993159119,"user_tz":-300,"elapsed":762,"user":{"displayName":"ahmed lone","userId":"02458088882398909889"}},"outputId":"2ce0b829-ecca-47a5-d842-65cf9871a84a"},"source":["ner_type_to_viz = 'ORG'\n","ner_df[ner_df.entities_ner_class == ner_type_to_viz]['entities_ner'].value_counts().plot.bar(title='Most often occuring ORG labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f4f0c36dfd0>"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}